Playlists by Year: A Tape Side's Worth of 1990

The best songs from the last year before grunge.




Playlists by Year: A Tape Side's Worth of 1982

Hey, it's the greatest songs and instrumental tracks visiting us from the year 1982!


Playlists by Year: A Tape Side's Worth of 2017

The best songs and greatest tracks of 2017.


Playlists by Year: A Tape Side's Worth of 1981

The best songs and greatest tracks from 1981. Lots of mainstream appeal this time round.



Playlists by Year: A Tape Side's Worth of 1979

Listen to punk slowly fading away on the Spotify playlist below.


Playlists by Year: A Tape Side's Worth of 1978

The Spotify Playlist with arguably the best songs from 1978


Playlists by Year: A Tape Side's Worth of 1977

The greatest songs of 1977. Featuring a surprisingly small proportion of punk songs.



Playlists by Year: A Tape Side's Worth of 1975

We've arrived in the mid-70s. Meager times. Nonetheless, here's some of the greatest music from 1975.


Playlists by Year: A Tape Side's Worth of 1970

The Spotify playlist with the greatest songs and instrumentals for the first year after the Gilded Second Half of the Sixties. A pretty good year.


Playlists by Year: A Tape Side's Worth of 1969

The Spotify playlists for one of pop's best years. The Abbey Road medley is one song, obviously.


Playlists by Year: A Tape Side's Worth of 1968

Some 43 minutes from one of the best years in music history.


Playlists by Year: A Tape Side's Worth of 1967

Perhaps the greatest French song ever, plus 11 more tracks of similar quality.


Playlists by Year: A Tape Side's Worth of 1966

The greatest songs of 1966, one of the best years in the history of pop.


Getting ahead in the Lucrative Field of Data Massaging

Evan Warfel has an excellent comment on a post of Andrew Gelman's. Reproduced in full:
Perhaps we are teaching statistics backwards. Instead of teaching students to try and come up with the correct result, we could teach what it feels like to rationalize one’s way through to non-objectivity.

A final exam question might go: This dataset consists of 5 completely uncorrelated variables — I’ve labeled the columns as ‘weight of cat’, ‘probability of attrition’, ‘color of cat [in RGB]’, ‘current age of subject’ and ‘SAT verbal score’. Find a way to make 3 statistically significant correlations and one non-significant correlation. You get an extra point for each spurious t-test you can come up with. The catch is that your entire analysis has to form part of a coherent story. Bonus points go to the 5 most concise answers.


Predictions Concerning Migration to Germany

1. The current love-fest, remindful of the opening of the Berlin Wall, will soon end and something in the range between disillusionment and xenophobia will set in. Like the post-reunification hangover, really, only on steroids, coke and speed.

2. Family reunification legislation (Familienzusammenf├╝hrung) will be severely tightened within the next three years.


Playlists by Year: A Tape Side's Worth of 1961

The greatest songs (and non-song tracks) from 1961, as far as I can tell.


Robin Hanson's Final Words on Signaling

"Falsifiability is just not a very useful concept in social science. Really."

A Two-Step Model of Class-typical Behaviour

Let's start with the example: In the U.S., high-SES people used to smoke more than low-SES people until about 1965. Then the lines crossed, once, and they never crossed again. These days, there are many high-SES people that you don't have to tell about health risks: to them, smoking is prole. And who wants to be prole?

More generally, there are many behaviours that low-SES people show more frequently than low-SES people, and vice-versa. Why? Let me propose a two-step model. First, there is some initial reason why a certain behaviour is shown more often by low-SES people. Then, the behaviour becomes associated with being low SES. Then, the behaviour is reduced even more by high-SES people. 

Smoking is, I think, a good example. Initially, high-SES people may have had access to better information, or have been better at processing the information, or had more self-control, or have put a higher value on health, or what have you, or all of the above. This created an initial smoking gap. This helped associate smoking with being prole. This, in turn, caused people who don't want to be seen as prole to smoke less.

In some cases, the reason for the initial reason could simply be chance.

The model implies that SES differences in smoking were easier to explain in terms of the psychological factors mentioned above (more self-control, etc.) in 1970 than today. Generalizing this is left as an exercise to the reader.


How to Keep Your Man Happy

1. Have sex with him when he wants to.

2. Don't question his respectability.

It seems to me these are the two rules that are true for almost every man, and at the same time are specific to keeping your man happy, rather than keeping your spouse happy.